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Wiometrics : Comparative Performance of Artificial Neural Networks for Wireless Navigation

Whiton, Russ (author)
Lund University,Lunds universitet,Kommunikationsteknologi,Forskargrupper vid Lunds universitet,LTH profilområde: AI och digitalisering,LTH profilområden,Lunds Tekniska Högskola,Communications Engineering,Lund University Research Groups,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH,Volvo Cars Corporation (VCC)
Chen, Junshi (author)
Lund University,Lunds universitet,Kommunikationsteknologi,Forskargrupper vid Lunds universitet,Communications Engineering,Lund University Research Groups
Tufvesson, Fredrik (author)
Lund University,Lunds universitet,Kommunikationsteknologi,Forskargrupper vid Lunds universitet,LTH profilområde: AI och digitalisering,LTH profilområden,Lunds Tekniska Högskola,LU profilområde: Naturlig och artificiell kognition,Lunds universitets profilområden,Communications Engineering,Lund University Research Groups,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH,LU Profile Area: Natural and Artificial Cognition,Lund University Profile areas
 (creator_code:org_t)
2024
2024
English 16 s.
In: IEEE Transactions on Vehicular Technology. - 0018-9545. ; , s. 1-16
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • Radio signals are used broadly as navigation aids, and current and future terrestrial wireless communication systems have properties that make their dual-use for this purpose attractive. Sub-6 GHz carrier frequencies enable widespread coverage for data communication and navigation, but typically offer smaller bandwidths and limited resolution for precise estimation of geometries, particularly in environments where propagation channels are diffuse in time and/or space. Nonparametric methods have been employed with some success for such scenarios both commercially and in literature, but often with an emphasis on low-cost hardware and simple models of propagation, or with simulations that do not fully capture hardware impairments and complex propagation mechanisms. In this article, we make opportunistic observations of downlink signals transmitted by commercial cellular networks by using a software-defined radio and massive antenna array mounted on a ground vehicle in an urban non line-of-sight scenario, together with a ground truth reference for vehicle pose. With these observations as inputs, we employ artificial neural networks to generate estimates of vehicle location and heading for various artificial neural network architectures and different representations of the input observation data, which we call wiometrics, and compare the performance for navigation. Position accuracy on the order of a few meters, and heading accuracy of a few degrees, are achieved for the best-performing combinations of networks and wiometrics. Based on the results of the experiments we draw conclusions regarding possible future directions for wireless navigation using statistical methods.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)

Keyword

Artificial neural networks
Artificial Neural Networks
Channel Estimation
Fingerprint recognition
Hardware
Navigation
Radio navigation
Radiowave Propagation
Wireless communication
Wireless sensor networks

Publication and Content Type

art (subject category)
ref (subject category)

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